Natural Gas Pipelines¶

Data Import¶

In [3]:
import pandas as pd
import matplotlib.pyplot as plt
import geopandas as gpd
import folium
import contextily as cx
import rtree
from zlib import crc32
import hashlib
from shapely.geometry import Point, LineString, Polygon

Natural Gas Pipeline Data¶

In [4]:
## Importing our DataFrames

#gisfilepath = "/Users/jnapolitano/Projects/data/energy/Natural_Gas_Pipelines.geojson"
gisfilepath = '/Users/jnapolitano/Projects/data/energy/Natural_Gas_Liquid_Pipelines.zip'

ng_pipeline_df = gpd.read_file(gisfilepath)

ng_pipeline_df = ng_pipeline_df.to_crs(epsg=3857)

#uniqe = ng_market_df.TYPE.unique()
ng_pipeline_df.dropna(inplace=True)
ng_pipeline_df.describe()
Out[4]:
Shape_Leng
count 33643.000000
mean 0.141669
std 5.455210
min 0.000000
25% 0.017489
50% 0.056064
75% 0.136343
max 1000.000000 
{eval-rst}

.. index::
   single: Natural Gas Pipeline  Map

Natural Gas Pipeline Map¶

In [5]:
ng_pipeline_map =ng_pipeline_df.explore(
    column="TYPEPIPE", # make choropleth based on "PORT_NAME" column
     popup=False, # show all values in popup (on click)
     tiles="Stamen Terrain", # use "CartoDB positron" tiles
     cmap='Reds', 
     #m=ng_market_map,# use "Set1" matplotlib colormap
     #style_kwds=dict(color="black"),
     #marker_kwds= dict(radius=6),
     tooltip=['TYPEPIPE','Operator'],
     legend =False, # use black outline)
     categorical=True,)

ng_pipeline_map
Out[5]:
Make this Notebook Trusted to load map: File -> Trust Notebook